Population-guided deformable registration

ABSTRACT

A registration technique is provided that can combine one or more related registrations to enhance accuracy of a registration of image volumes. A registration relationship between a first source volume and a target volume and a registration relationship between the first source volume and a second source volume are concatenated to provide an estimate of a registration relationship between the second source volume and the target volume. The estimate is utilized to inform the direct registration of the second source volume to the target volume or utilized in place of the direct registration.

TECHNICAL FIELD

The present disclosure relates to deformable image registration and,more particularly, to an apparatus and method for population-guideddeformable registration of medical images.

BACKGROUND

Image registration is a determination of a transformation that alignslocations in one image to locations in another image. In medicalimaging, registration is particularly useful to integrate or otherwisecorrelate information acquired from disparate images. For example,patients images acquired at different times can be compared tounderstand disease progression, to design therapy strategies, todetermine treatment effectiveness, and so on. In another example,patient images can be compared with various reference images (e.g.,atlases, normal templates, disease templates, etc.) for diagnosticpurposes. While medical imaging technology enables unique views of apatient to be obtained, registration of medical images increases thepower of the information.

In a clinical setting, medical image registration can involveintra-subject registration (registration of different images of onepatient) and/or inter-subject registration (between different images ofdifferent patients or templates). For intra-subject medical imaging, forinstance, goals can include tracking disease progression, verifyingtreatment success, updating contoured or segmented volumes of interest,updating therapy plans, and the like. To achieve these goals, multipleimages of a patient can be acquired at different times (e.g., daily,weekly, monthly, etc.). Due to variable patient positioning duringimaging, changes due to disease progression or response to therapy,systematic errors in the imaging apparatus, and/or general randomness,the images are not perfectly aligned. Registration corrects themisalignment such that corresponding locations, anatomical structures,etc., between the images are correlated.

According to one example, newly acquired images are registered to apreviously acquired image. In this example, each successively obtainedimage is registered to the same reference image. The reference image canbe, for example, a first image acquired of the patient. However, it isto be appreciated that the reverse procedure (i.e., registeringpreviously acquired images or reference images to newly acquired images)can be considered as registering in either direction, i.e. old to new ornew to old, provides similar benefits.

Alternatively, each image of the patient can be registered to areference image not associated with the patient, i.e. an inter-subjectregistration. For instance, the reference image can be an image selectedfrom a library, such as a representative image or an image generated asa composite or average of multiple images from the library.

However, with any of the techniques described above, a patient image isdirectly, and individually, registered to the reference image(s) or thereference image(s) are directly and individually registered to thepatient image. Errors in identifying corresponding anatomy in twoimages, i.e. registration errors, can result from differences in howanatomical information is represented in the two images, from imagingsystem differences, from patient configuration difference, differencesin anatomy between different patients, etc. Positional alignmentdifferences can result from registrations of successively obtainedimages of a patient, even when registered to an identical target.

Accordingly, there is a need for accurate registration mechanisms thatreduce variability of alignments from systematic, random, andconfiguration-based effects.

SUMMARY

A simplified summary is provided herein to help enable a basic orgeneral understanding of various aspects of exemplary, non-limitingembodiments that follow in the more detailed description and theaccompanying drawings. This summary is not intended, however, as anextensive or exhaustive overview. Instead, the sole purpose of thesummary is to present some concepts related to some exemplarynon-limiting embodiments in a simplified form as a prelude to the moredetailed description of the various embodiments that follow.

In various, non-limiting embodiments, a registration technique isprovided that can combine one or more related registrations to enhancethe accuracy of a registration of a two images. To illustrate, considerthree image volumes: a target, a first source, and a second source.Given these image volumes, three registration relationships (or sixrelationships when inverse relationships are included) can bedetermined. These relationships include the registration of the firstsource to the target, the registration of the second source to thetarget, and the registration of the second source to the first source.Each of these relationships is typically determined via a direct andindividual relationship. However, according to an aspect, theregistration relationship between the first source and the target andthe registration relationship between the first source and the secondsource can be concatenated or combined in some way to provide anestimate of the registration relationship between the second source andthe target. The concatenated relationship can be utilized as a finalregistration relationship between the second source and the target.Alternatively, the estimate is utilized to inform the determination ofthe actual registration relationship between the second source and thetarget, via direct registration.

This and other embodiments are described in more detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects are better understood when the followingdetailed description is read with reference to the accompanyingdrawings, in which:

FIG. 1 is a block diagram of an exemplary, non-limiting imageregistration system configured to implement population-guidedregistration of image volumes;

FIG. 2 is a flow diagram illustrating an exemplary, non-limitingembodiment for registering two image volumes based on an estimatederived from related registration relationships;

FIG. 3 is a block diagram illustrating a foundation for an estimatedregistration relationship;

FIG. 4 is an exemplary, non-limiting illustration of utilizing anestimated registration relationship to register two image volumes;

FIG. 5 illustrates an exemplary, non-limiting registration, such as adeformable registration;

FIG. 6 is a block diagram of an exemplary, non-limiting imageregistration system that determines deformable registrations inaccordance with one or more aspects;

FIG. 7 is a block diagram of an exemplary, non-limiting framework fordeformable registrations in accordance with one or more aspects;

FIG. 8 is a block diagram of an exemplary, non-limiting imageregistration system according to an aspect;

FIG. 9 is a flow diagram illustrating an exemplary, non-limitingembodiment for iteratively generating intra-library registrationrelationships;

FIG. 10 is a flow diagram illustrating an exemplary, non-limitingembodiment for utilizing a virtual reference template;

FIG. 11 is a block diagram of an exemplary, non-limiting imageregistration system configured to execute multiple, simultaneousregistrations of an image volume to a plurality of reference imagevolumes; and

FIG. 12 illustrates a block diagram of an exemplary, non-limitingcomputing device or operating environment in which one or more aspectsof various embodiments described herein can be implemented.

DETAILED DESCRIPTION

Registration of medical images is typically conducted withoutconsideration of corollary data acquired through registration ofdisparate, but related, medical images. Accordingly, various errors inidentifying corresponding anatomy in different medical images are leftunchecked. In various, non-limiting embodiments, population forces areintroduced in the registration of two or more image volumes. Thepopulation forces further influence registration of the two or moreimage volumes beyond traditional internal forces (e.g., regularization,bendability, etc.) and image forces (e.g., image matching metrics) toimprove accuracy of the resultant relationship.

According to an example, relationships associated with related imagesare utilized to guide the registration of the two or more image volumes,thus providing the aforementioned population forces. More specifically,registration relationships can be concatenated, or chained, such that anestimate of a direct relationship results. According to one aspect, theconcatenated relationships are utilized as a final registrationrelationship. In accordance with an alternative aspect, the estimate canbe utilized when computing the actual direct registration relationship.

In addition to providing more accurate relationships, population-guidedregistrations enable generation of a reference template image volume,which can be virtual. That is, the reference template image volume,itself, does not contain image data. The reference template image volumeprovides a common template space, which enables appropriate correlationof anatomical structures, while also mitigating template selection bias(i.e., a tendency of a determined registration relationship thatcorrelate a source image to a selected template regardless of accuracy).

In a further aspect, population-guided registration facilitatesmultiple, simultaneous registration of a source image to a plurality ofreference volumes. In some clinical situations, it is desirable toregister medical images to a common template space in order tofacilitate statistical comparison with normal or diseased databases.However, if images corresponding to different disease states differsignificantly, it can be difficult to, for example, register an image ofa diseased patient to a reference or template of a normal control.Additionally, having a set of templates respectively associated withdifferent disease states ensures that a template sufficiently similar tothe source image exists, but registering two patients to differentreference images introduces selection bias. Accordingly, by providingthe plurality of reference volumes to a common template space, thesource image can be registered to the common template space based on acombination of registration-related information gleaned throughindividual or combined registrations to each of the reference volumes.This can be achieved by a variety of techniques. For instance, thesource image can be individually registered to each reference volume andthe individual registrations can be combined in some manner. Accordingto another technique, final registration parameters are determined as abalance of registration parameters, as such parameters would bedetermined individually, between the source image and each referencevolume. As each reference volume exerts a consistent influence on afinal registration for all source images, selection bias is mitigated.

In one embodiment, an image registration system is described herein thatinclude a registration engine. The registration engine is configured toobtain a first set of registration relationships respectively mappingbetween respective source images of a set of images and at least onetarget image, determine a second set of registration relationshipsbetween respective pairs of source images in the set of images, generatea set of estimated registration relationships via respectivecombinations of a relationship from the first set with a relationship ofthe second set, and register at least one source image from the set ofimages to the at least one target image based in part on an associatedestimated registration relationship from the set of estimatedregistration relationships. The image registration system can alsoinclude a computer-readable storage medium. The registration engine canbe implemented as computer-executable instructions stored on thecomputer-readable storage medium. In an example, the registration enginedetermines deformable registrations.

In another example, the registration engine utilizes the associatedestimated registration relationship as a final registration relationshipbetween the at least one source image and the at least one target image.A combination of a first registration relationship with a secondregistration relationship correlates a first location of a first imageto a second location of a second image via a third location of a thirdimage, wherein the first registration relationship maps the firstlocation of the first image to the third location of the third image andthe second registration relationship maps the third location of thethird image to the second location of the second image.

In yet another example, the registration engine is further configured todetermine an initial set of registration relationships betweenrespective pairs of source images in the set of images, the initial setbeing distinct from the second set of registration relationships.Pursuant to this example, the registration engine is further configuredto combine respective pairs of registration relationships from theinitial set to build an initial set of estimated registrationrelationships between respective pairs of source images in the set ofimages. Moreover, the registration engine is further configured torefine the initial set of registration relationships based on theinitial set of estimated registration relationships to produce a refinedset of registration relationships. The registration engine can alsoiteratively combine respective pairs of registration relationships fromthe refined set and update the refined set based on results of acombination.

According to another example, the at least one target image is aplurality of target images registered to a common space and theregistration engine is further configured to register source images ofthe set of images to the plurality of target images to generate a finalregistration relationship between the source images and the commonspace, the final registration relationship is based on respectiveregistration relationships between the source images and each targetimage. Alternatively, the registration engine is further configured toregister source images of the set of images to the plurality of targetimages to generate a final registration relationship between the sourceimages and the common space, the final registration relationship isbased on respective registration parameters between the source imagesand each target image. In this alternative, the source images areregistered to the common space according to an equal contributinginfluence from each of the plurality of target images as a result ofregistration based on the respective registration parameters.

In further examples, the registration engine is further configured todetermine, simultaneously and iteratively, the first set of registrationrelationships and the second set of registration relationships. Theregistration engine can also be configured to guide a registration ofthe at least one source image from the set of images to the at least onetarget image towards the associated estimate registration relationship.In addition, the computer-readable storage medium further stores thereona library comprising the set of images. After registration of the atleast one source image to the at least one target image, a resultantregistration relationship and the at least one target image are added tothe library.

According to another embodiment, described herein is a method ofregistering image volumes. The method can include registering a firstimage volume to a second image volume and registering a third imagevolume to the first image volume. The method also includes combining afirst registration relationship, between the first image volume and thesecond image volume, with a second registration relationship, betweenthe third image volume to the first image volume, such that acombination of the first and second registration relationship is anestimated registration relationship between the third image volume andthe second image volume. In addition, the method can include registeringthe third image volume to the second image volume based at least in parton the estimated registration relationship. In an example, the firstimage volume, the second image volume, and the third image volume aremedical images.

In a further aspect, registering can include performing a deformableregistration operation. The first image volume and the third imagevolume are stored in a common library and the method can includepre-registering the third image volume to the first image volume. Themethod can additional include storing a resultant registrationrelationship in the common library.

In yet another embodiment, a method of generating a reference templatefor registration is described. The method can include respectivelyregistering pairs of image volumes of a library of image volumes togenerate a first set of intra-library registration relationships,concatenating each registration relationship from the first set ofintra-library registration relationships to a selected relationship tobuild a set of estimated registration relationships to the referencetemplate, and registering image volumes to the reference template basedon the set of estimate registration relationships. In an example, themethod can also include iteratively concatenating and registering togenerate improved accuracy. According to an aspect, for a firstiteration, the selected relationship is an identity relationship.

An overview of some embodiments for an image registration system andassociated methods has been presented above. These and other exemplary,non-limiting embodiments are hereinafter described in more detail.

Referring now to FIG. 1, illustrated is an exemplary, non-limiting imageregistration system 100 configured to implement population-guidedregistration of image volumes. Image registration system 100 determines,for a pair of image volumes, a registration relationship that specifiesa transform, or parameters of a transform, by which one image volume ofthe pair is brought into alignment with the other image volume. Inparticular, image registration system 100 generates registrationrelationship(s) 130 from various image volumes input. For example, asshown in FIG. 1, a source image (s₁) 110, a source image (s2) 112, and atarget image (t) 120 are input to image registration system 100. Thoughshown in FIG. 1 as being input in parallel, it is to be appreciated thatthe various image volumes can be input at different times.

As utilized herein, a target image volume such as target image 120specifies a reference volume or template volume to which other imagevolumes are to be aligned via registration. A source image volume (e.g.,source images 110 and 112) specifies the image volume that undergoes atransform, determined via registration, to align with the target imagevolume. Though described here that the transforms computed aredirectional from a source to a target, it is to be appreciated that thevarious transforms and registration relationships can be computed asdirectional from a target to a source or may be invertible such thatdirectionality is unimportant.

According to an example, registration relationships 130 can include afirst registration relationship between source image 110 and targetimage 120, a second registration relationship between source image 112and source image 110, and a third registration relationship betweensource image 112 and target image 120. In conventional registrationsystems, the second registration relationship between source image 112and source image 110 is typically never determined as conventionalsystems often consider that relationship superfluous. For instance,source image 110 and source image 112 can be images of a subject takenat different times. Conventional applications look to register thoseimages to a common target, e.g., target image 120, to enable comparisonand do not register the images with each other. However, as shown inFIG. 1, registration relationships 130, determined by image registrationsystem 100, feedback to image registration system 100, which utilizesthe generated registration relationships 130 to determine otherrelationships.

Turning to FIG. 2, illustrated is a technique by which imageregistration system 100 utilizes already generated registrationrelationships to determine new relationships according to onenon-limiting, exemplary embodiment. At 200, a first registrationrelationship between a first image volume (e.g., source image 110, alsodenoted herein as “s₁”) and a second image volume (e.g., target image120 or “t”) is determined. The first registration relationship specifiesa transform, or parameters of a transform, whereby locations,structures, landmarks, regions, sub-regions, pixels, voxels, etc. in thefirst image volume are mapped to corresponding locations, structures,landmarks, regions, sub-regions, pixels, voxels, etc. of the secondimage volume. At 202, a second registration relationship between a thirdimage volume (e.g., source image 112, also denoted herein as “s₂”) andthe first image volume s₁ is determined.

At 204, an estimate of a third registration relationship between thethird image volume s₂ and the second image volume t is generated.Specifically, the first registration relationship and the secondregistration relationship are combined to generate the estimate. Turningbriefly to FIG. 3, illustrated is a block diagram depicting the relativearrangement of the first, second, and third registration relationships.As shown in FIG. 3, the first registration relationship R_(t-s1)provides a mapping between corresponding locations of image volumes s₁and t, the second registration relationship R_(s1-s2) specifies acorrespondence between locations of image volumes s₂ and s₁, and thethird registration relationship R_(t-s2) provides a mapping betweencorresponding locations of image volumes s₂ and t.

Intuitively, as a registration relationship identifies a location of animage volume and maps the location to a corresponding location inanother image volume, one exemplary and non-limiting combinationtechnique, referred to herein as a concatenation, can readily beillustrated. The dashed lines of FIG. 3 provide a path alongregistration relationships by which the estimate of the thirdregistration relationship is derived. In other words, the dashed linerepresents a concatenation. Given a location x_(s2) in image volume s₂,a corresponding point x_(s1) in image volume s₁ can be determined viathe second registration relationship R_(s1-s2). Via the firstregistration relationship R_(t-s1), a point x_(t) in the target imagevolume t corresponding to point x_(s1) in image volume s₁ is known.Accordingly, via the first and second registration relationship, i.e.,via the concatenation thereof, a point in the target image volume tcorresponding to the point x_(s2) in image volume s₂ can be estimated.

Referring back to FIG. 2, at 206, the estimate is utilized to guide adirect determination of the third registration relationship. In additionto illustrating concatenation as described above, FIG. 4 further depictsthe utilization of the estimate to provide population-guidedregistration in accordance with one or more aspects. As shown in FIG. 4,locations in source image s₁ and target image t corresponding tolocation x of source image s₂ are linked via various registrationrelationships. As described above, the estimated registrationrelationship R′_(t-s2) is generated by a concatenation of the first andsecond registration relationship. That is, the estimated registrationrelationship R′_(t-s2) maps location x to the location indicated bystarting from location x, traversing along the second registrationrelationship R_(s1-s2), and then traversing the first registrationrelationship R_(t-s1).

Also shown in FIG. 4 is a naively generated registration relationshipbetween the source image s₂ and the target image t. The naivelygeneration relationship is determined without consideration ofpopulation forces, i.e., without utilizing the estimated relationshipR′_(t-s2). As shown in the simplified example of FIG. 4, by introducingpopulation forces, i.e. by considering the information carried in theestimated relationship, a more accurate registration can be achieved. Itis to be appreciated that FIG. 4 illustrates a high-level, generalizedexample of utilizing an estimated registration, and that the subjectclaims are not intended to be limited to estimated registrations merelybeing averaged with naively generated registrations. An alternativeimplementation, in connection with a non-limiting, exemplaryregistration technique is provided later.

As included in the above-described embodiments, the terms “image volume”and “registration” are generally used to indicate substantially anythree-dimensional or two-dimensional image data and substantially anytype of registration transform (e.g., rigid, affine, deformable, etc.),respectively. Accordingly, the concepts of concatenating registrationrelationships to derive estimated registrations and utilizing theestimated registrations to improve direct registrations can be appliedto a variety of image data and registration types. In the exemplaryembodiments described below, examples are provided with respect todeformable registrations of medical images. One of ordinary skill in theart would appreciate that the aspects and examples described below areapplication to other image data and registration transforms.

A deformable registration typically involves a non-linear densetransformation or a spatially varying deformation field. FIG. 5illustrates a simplified example of a transformation associated with adeformable registration. As shown, with deformable registrations, objecttransformations need not be limited to rotations, scaling, or otheraffine transformations. With deformable registrations, a number ofdegrees of freedom available to specify transformations can range fromten to millions.

Turning to FIG. 6, illustrated is an exemplary, non-limiting imageregistration system 600 that generates a deformable registrationrelationship 606 between a source image (s₂) 620 and a target image (t)622 in accordance with one or more aspects. In accordance with anexample described herewith, a source image (s₁) 624 is assumed to havebeen previously registered to target image 622 by the image registrationsystem 600 and also registered to source image 620 by the imageregistration system 600. The image registration system 600 can include aregistration data store 602 that retains the previously determinedrelationships, R_(s1-s2) and R_(t-s1), as well as other registrationsalready computed. Further, data store 602 can store newly generatedrelationships for use in later registrations.

As shown in FIG. 6, image registration system 600 also includes aregistration engine 610 configured to determine registrationrelationships between at least two image volumes. The registrationengine 610 includes several functional blocks configured to contributeor influence an overall registration relationship output by the imageregistration system 600. An image matching module 612 determines animage-based displacement for every voxel of source image 620 relative totarget image 622. According to an example, the image matching module 612can compare voxel intensities between the source image volume, e.g.source image 620, and the target image volume, e.g. target image 622.More particularly, in accordance with an aspect, image matching module612 executes an optimization of an energy minimization problem. Forinstance, image matching module 612 can apply a gradient descent on asum of square intensity differences of voxels. However, it is to beappreciated that other techniques can be utilized such as, but notlimited to, an efficient minimization, or other fitness criteria can beapplied.

A regularization module 614 is provided to constrain the displacementsdetermined by the image matching module 612. Constraints imposed orenforced by the regularization module 614 facilitate ensuring thatinternal forces are obeyed in a reasonable manner. For instance, theregularization module 614 can prevent folding by ensuring that a givenvoxel does not displace outside of its neighbors. Regularization module614 can provide such constraints in a variety of manners. For instance,regularization module 614 can directly add a term that influences theobjective function being optimized. Alternatively, regularization module614 can implement filtering, such as Gaussian smoothing or splinefitting, to constrain displacements. It is to be appreciated that othersuitable regularization techniques can be utilized herein without limitto the techniques described above.

A population control module 616 is provided to further refinedisplacements of voxels based on estimated registrations derived frompreviously determined relationships. For example, an estimation module604 can generate an estimated relationship R′_(t-s2) based on previouslygenerated relationships R_(s1-s2) and R_(t-s1). The population controlmodule 616 applies the estimated relationship to determination made bythe image matching module 612 and/or regularization module 614 to outputthe final registration relationship 606. Population control module 616can provide such constraints in a variety of manners. For instance,population control module 616 can directly add a term that influencesthe objective term being optimized. Alternatively, population controlmodule 616 can implement a bias term to constrain displacements aftereach iteration of an optimization.

While FIG. 6 depicts registration engine 610 as having disparatefunctional modules performing individual actions, it is to beappreciated that the functional modules, e.g. image matching module 612,regularization module 614, and the population control module 616 can beimplemented as a single functional module. According to another aspect,the registration engine 610 can be implemented such that one or morefunctions of the image matching module 612, regularization module 614,and the population control module 616 are simultaneously and jointlyperformed.

By way of example, consider a registration framework 700 shown in FIG.7, which can be one possible implementation of the registration engine610. Deformable registrations can be determined by an optimizationstrategy. A source image volume 704 is modified by a transform 712 inaccordance with a set of initial transform parameters. In one example,transform 712 can initialize to perform an identity transform on sourceimage volume 704 to begin the optimization that leads to a finalregistration relationship. The source image volume 704, as modified bythe transform 712, and the target image volume 702 are compared by anevaluator 708 which outputs a fitness value. The evaluator 708determines the fitness value in accordance with a predeterminedfunction, which can be based on voxel properties of source image volume704 and target image volume 702. The fitness value is input to anoptimizer 710 which updates a set of transform parameters utilized bythe transform 712. The optimizer 710 determines the updated transformparameters according to the fitness value and an objective function.This process from transform 712 to evaluator 708 to optimizer 710 can beiteratively repeated until a convergence criterion is satisfied. Forinstance, when the optimizer 710 determines that no furthermodifications to the transform parameters will result in an improvement,the loop breaks and the final registration relationship (i.e., set oftransform parameters) is output.

With the above described framework, the various functions of the imagematching module 612, regularization module 614, and the populationcontrol module 616 can be mathematically introduced into the optimizer710 and/or the evaluator 708. For example, regularization can beaccomplished by introducing a regularization term to a function utilizedby the evaluator 708. This regularization term adjusts a determinedfitness value beyond the influences of only image-based forces (i.e.,voxel properties). Accordingly, the regularization term facilitatescurtailing extreme displacements of voxels relative to displacements ofneighboring voxels. In another example, regularization can be achievedthrough modification of the objective function employed by the optimizer710. Similarly, population forces can be introduced by modifying theevaluator function and/or the objective function. For instance, theobjective function of the optimizer 710 can consider estimatedrelationships provided by concatenations of related registrations, asdescribed above. According to one example, estimated relationships canbe averaged and adjustments considered by the optimizer 710 can bepenalized by the objective function based on a distance between theaverage estimated registration and the proposed registration, afteradjustment.

In another example, it is possible that estimated registrations clusterat disparate candidate registrations, particularly as a number ofdisparate source volumes involved in concatenation increases.Accordingly, the objective function can be devised to encourageregistration adjustments converging on any of the clusters. Forinstance, the objective function can penalize adjustments based on atotal distance to a proposed registration relationship from the closestN estimations (clusters), where N is an integer greater than or equal toone.

The exemplary embodiments presented above are described relative to anexample of a single target volume and two source volumes. It is to beappreciated that the above described embodiments can be extended to setsof target volumes and sets of source volumes. For example, a library ofsource image volumes can be built over time as a patient is successivelyscanned to track progression of a disease and/or treatment. As eachvolume is obtained, it can be registered to a target volume. However, asit is likely each source image volume undergoes a similar registration,it is desirable to glean any information learned from the priorregistrations to the target volume. Accordingly, the population-guidedregistration techniques described above can be applied in thissituation.

Referring to FIG. 8, a non-limiting, exemplary system is illustratedthat includes image registration system 600 described above. Accordingto this embodiment, an image library 810 is provided that includes alibrary of source image volumes acquired from one subject or multiplesubjects. In conventional systems, registering the image library 810 toa target image volume 820 involves computing a set of registrationrelationships, each of which maps one source image volume of the imagelibrary 810 to the target image volume 820. However, image registrationsystem 600 performs population-guided registration as describedpreviously. To enable population guiding, a set of intra-libraryregistration relationships are generated by the image registrationsystem 600. The set of intra-library registration relationships mayinclude a registration relationship for every pair of source imagevolumes in image library 810. It is to be appreciated that inverserelationships can be included or excluded, as desired.

As with the conventional system, the image registration system 600 cangenerate, with or without population control, a set of relationshipsbetween each source image volume of image library 810 and the targetimage volume 820. With the two sets, the image registration system 600can build a set of estimate relationships by concatenating each memberof the set of intra-library relationships with each relevant member ofthe set of relationships to the target.

In accordance with another aspect, it is to be appreciated that the setof intra-library relationships and the set of relationships to thetarget need not be naively generated without population control. Turningto FIG. 9, an exemplary embodiment for iteratively building the set ofintra-library registrations, with population control, is illustrated. At900, the process is bootstrapped by generating a set of naïve,non-population-guided intra-library registration relationships for eachpair of sources in the image library 810. At 902, pairs of registrationrelationships from the naively generated set are concatenated to build aset of estimated intra-library registration relationships for each pairof sources in the image library 810. At 904, the set of estimatedrelationships is employed to generate a set of population-guidedregistration relationships. At 906, the process can iterate, i.e. returnto 902, to successively build the set of intra-library registrationrelationships with increasingly sophisticated estimations.

A similar technique can be utilized to build the set of registrationrelationships between the target image volume 820 and each source imagevolume in the image library 810, wherein this set of registrationrelationships is simultaneously and iteratively built in parallel withthe intra-library relationships. In this instance, registrationrelationship concatenated to form estimates can be generated and storedat a course resolution to increase resource efficiency.

Referring back to FIG. 8, the target image volume 820 and allregistration relationship related thereto can be added to image library810. Accordingly, when a new source image volume 830 is acquired,population-derived information associated with the target image volume820 and the sources of image library 810 is available to enable anenhanced registration of the new source image volume 830 to the targetimage volume 820. It is to be appreciated that, to further enable theutilization of population-derived information in the registration of thenew source image volume 830 to the target image volume 820, the sourceimage volume 830 is registered to each image volume in the image library810 (or vice versa). The registration relationships between the newsource image volume 830 and the volumes of the image library 810 can bedetermined in parallel with the relationships mentioned above, or can bedetermined afterwards as the new source image volume 830 becomesavailable, for example.

According to yet another aspect, population-guided registration enablesregistration to a virtual reference template volume, which can be voidof any image data itself. While reference templates can be essentiallyany image volume such as a single image volume, an image volume selectedas being the most representative, an average of a set of image volumesin a library, or the like, these conventional template selectiontechniques can introduce template or selection bias to subsequentregistrations. Turning to FIG. 10, which illustrates an embodiment foriteratively building the virtual template, a set of naïve,non-population-guided intra-library registration relationships for eachpair of sources in the image library 810 is generated at 1000. At 1002,each registration relationship from the set are concatenated with aselected registration relationship, which can be an identityregistration relationship or any other computed registrationrelationship to a real or virtual template to generate a set ofestimated registration relationships to the template. At 1004, a set ofpopulation-guided registrations to the template are generated based onthe estimates. At 1006, the process is iterated with each successivelygenerated set of refined relationships. Moreover, with each iteration,it is to be appreciated that the selected registration relationship canbe updated or otherwise re-selected as the iterative registrations honein on the virtual template.

In a further aspect, population-guided registrations can be extended toenable multiple, simultaneous registration of a source image to aplurality of reference volumes. FIG. 11 illustrates an imageregistration system 1100 configured to execute multiple, simultaneousregistrations of a patient image volume 1110 to a plurality of referenceimage volumes 1120. Each of the plurality of reference image volumes1120 is registered to a common space. Image registration system 1100 isconfigured to execute a modification of population-guided registrationto simultaneously register the patient image volume 1110 to theplurality of reference images 1120. To highlight this technique,consider a simplified example of an affine registration. During theregistration and, specifically, during the optimization of registrationparameters, respective registration information of the patient imagevolume 1110 to each of the reference image volumes can be average toform a final score subject to optimization. Accordingly, each referenceimage volume exerts an equal influence on registration of the patientimage volume 1110. Alternatively, the image matching scores between thepatient image 1110 and the plurality of reference images can becombined, e.g. averaged, to compute a single matching score for use byan optimization engine. For deformable registrations, an average of asimilarity of a subregion around a proposed patient landmark comparedwith a subregion around a corresponding landmark of each reference imagevolume can be computed. Alternatively, during optimization, eachproposed registration relationship can be used to compute a matchingscore to each reference image and these scores combined, e.g. averaged,and optimization performed on this combined score.

Exemplary Computing Device

Referring now to FIG. 12, a high-level illustration of an exemplarycomputing device 1200 that can be used in accordance with the systemsand methodologies disclosed herein is illustrated. The computing device1200 includes at least one processor 1202 that executes instructionsthat are stored in a memory 1204. The instructions may be, for instance,instructions for implementing functionality described as being carriedout by one or more components discussed above or instructions forimplementing one or more of the methods described above. The processor1202 may access the memory 1204 by way of a system bus 1206.

The computing device 1200 additionally includes a data store 1208 thatis accessible by the processor 1202 by way of the system bus 1206. Thecomputing device 1200 also includes an input interface 1210 that allowsexternal devices to communicate with the computing device 1200. Forinstance, the input interface 1210 may be used to receive instructionsfrom an external computer device, from a user, etc. The computing device1200 also includes an output interface 1212 that interfaces thecomputing device 1200 with one or more external devices. For example,the computing device 1200 may display text, images, etc. by way of theoutput interface 1212.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 1200 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 1200.

While methodologies are described herein as being a series of acts thatare performed in a sequence, it is to be understood and appreciated thatthe methodologies are not limited by the order of the sequence. Forexample, some acts can occur in a different order than what is describedherein. In addition, an act can occur concurrently with another act.Further, in some instances, not all acts may be required to implement amethodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable storage medium or media. Thecomputer-executable instructions can include a routine, a sub-routine,programs, a thread of execution, and/or the like. Still further, resultsof acts of the methodologies can be stored in a computer-readablestorage medium, displayed on a display device, and/or the like.

The term “or” is intended to mean an inclusive “or” rather than anexclusive “or.” That is, unless specified otherwise, or clear from thecontext, the phrase “X employs A or B” is intended to mean any of thenatural inclusive permutations. That is, the phrase “X employs A or B”is satisfied by any of the following instances: X employs A; X employsB; or X employs both A and B. In addition, the articles “a” and “an” asused in this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromthe context to be directed to a singular form.

Further, as used herein, the term “exemplary” is intended to mean“serving as an illustration or example of something.” For the avoidanceof doubt, the subject matter disclosed herein is not limited by suchexamples. In addition, any aspect or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs, nor is it meant to precludeequivalent structures and techniques known to those of ordinary skill inthe art. Furthermore, to the extent that the terms “includes”, “has”,“contains”, and other similar words are used herein, such terms areintended to be inclusive in the a manner similar to the term“comprising” as an open transition word without precluding anyadditional elements when employed in a claim.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes both computer-readable storage media and communicationmedia including any medium that facilitates transfer of a computerprogram from one place to another. A computer-readable storage media canbe any available media that can be accessed by a computer. By way ofexample, and not limitation, such computer-readable storage media cancomprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to carry or store desired program code in theform of instructions or data structures and that can be accessed by acomputer. Disk and disc, as used herein, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk, andblu-ray disc (BD), where disks usually reproduce data magnetically anddiscs usually reproduce data optically with lasers. Further, apropagated signal is not included within the scope of computer-readablestorage media. Also, a connection can be a communication medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a coaxial cable, fiber optic cable, twisted pair,digital subscriber line (DSL), or wireless technologies such asinfrared, radio, and microwave, then the coaxial cable, fiber opticcable, twisted pair, DSL, or wireless technologies such as infrared,radio and microwave are included in the definition of communicationmedium. Combinations of the above should also be included within thescope of computer-readable media.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims.

What is claimed is:
 1. An image registration system, comprising: aregistration engine configured to: obtain a first set of registrationrelationships respectively mapping between respective source images of aset of images and at least one target image; determine a second set ofregistration relationships between respective pairs of source images inthe set of images; generate a set of estimated registrationrelationships via respective combinations of a relationship from thefirst set with a relationship of the second set; and register at leastone source image from the set of images to the at least one target imagebased in part on an associated estimated registration relationship fromthe set of estimated registration relationships; and a computer-readablestorage medium, wherein the registration engine comprisescomputer-executable instructions stored on the computer-readable storagemedium.
 2. The image registration system of claim 1, wherein theregistration engine determines deformable registrations between images.3. The image registration system of claim 1, wherein the registrationengine utilizes the associated estimated registration relationship as afinal registration relationship between the at least one source imageand the at least one target image.
 4. The image registration system ofclaim 1, wherein a combination of a first registration relationship witha second registration relationship correlates a first location of afirst image to a second location of a second image via a third locationof a third image, wherein the first registration relationship maps thefirst location of the first image to the third location of the thirdimage, and the second registration relationship maps the third locationof the third image to the second location of the second image.
 5. Theimage registration system of claim 1, wherein the registration engine isfurther configured to determine an initial set of registrationrelationships between respective pairs of source images in the set ofimages, the initial set being distinct from the second set ofregistration relationships.
 6. The image registration system of claim 5,wherein the registration engine is further configured to combinerespective pairs of registration relationships from the initial set tobuild an initial set of estimated registration relationships betweenrespective pairs of source images in the set of images.
 7. The imageregistration system of claim 6, wherein the registration engine isfurther configured to refine the initial set of registrationrelationships based on the initial set of estimated registrationrelationships to produce a refined set of registration relationships. 8.The image registration system of claim 7, wherein the registrationengine is further configured to iteratively combine respective pairs ofregistration relationships from the refined set and update the refinedset based on results of a combination.
 9. The image registration systemof claim 1, wherein the at least one target image is a plurality oftarget images registered to a common space, and wherein the registrationengine is further configured to register source images of the set ofimages to the plurality of target images to generate a finalregistration relationship between the source images and the commonspace, the final registration relationship is based on respectiveregistration relationships between the source images and each targetimage.
 10. The image registration system of claim 1, wherein the atleast one target image is a plurality of target images registered to acommon space, and wherein the registration engine is further configuredto register source images of the set of images to the plurality oftarget images to generate a final registration relationship between thesource images and the common space, the final registration relationshipis based on respective registration parameters between the source imagesand each target image.
 11. The image registration system of claim 10,wherein, as a result of registration based on the respectiveregistration parameters, the source images are registered to the commonspace according to an equal contributing influence from each of theplurality of target images.
 12. The image registration system of claim1, wherein the registration engine is further configured to determine,simultaneously and iteratively, the first set of registrationrelationships and the second set of registration relationships.
 13. Theimage registration system of claim 1, wherein the registration engine isfurther configured to guide a registration of the at least one sourceimage from the set of images to the at least one target image towardsthe associated estimate registration relationship.
 14. The imageregistration system of claim 1, wherein the computer-readable storagemedium further stores thereon a library comprising the set of images,and wherein, after registration of the at least one source image to theat least one target image, a resultant registration relationship and theat least one target image are added to the library.
 15. A method ofregistering image volumes, comprising: registering a first image volumeto a second image volume; registering a third image volume to the firstimage volume; combining a first registration relationship, between thefirst image volume and the second image volume, with a secondregistration relationship, between the third image volume to the firstimage volume, such that a combination of the first and secondregistration relationship is an estimated registration relationshipbetween the third image volume and the second image volume; andregistering the third image volume to the second image volume based atleast in part on the estimated registration relationship.
 16. The methodof claim 15, wherein registering comprises performing a deformableregistration operation.
 17. The method of claim 15, wherein the firstimage volume, the second image volume, and the third image volume aremedical images.
 18. The method of claim 15, wherein the first imagevolume and the third image volume are stored in a common library, themethod further comprising pre-registering the third image volume to thefirst image volume.
 19. The method of claim 18, further comprisingstoring a resultant registration relationship in the common library. 20.A method of generating a reference template for registration,comprising: respectively registering pairs of image volumes of a libraryof image volumes to generate a first set of intra-library registrationrelationships; concatenating each registration relationship from thefirst set of intra-library registration relationships to a selectedrelationship to build a set of estimated registration relationships tothe reference template; and registering image volumes to the referencetemplate based on the set of estimate registration relationships. 21.The method of claim 20, further comprising iteratively concatenating andregistering to generate improved accuracy.
 22. The method of claim 21,wherein, for a first iteration, the selected relationship is an identityrelationship.